Safe AI Initiative: A Call for Transparency of the Impact of Generative AI and Big Data

Authors

  • Jamie Kemman Shepherd University
  • Alani White Shepherd University
  • Holly White Shepherd University
  • Weidong Liao Shepherd University
  • Osman Guzide Shepherd University

DOI:

https://doi.org/10.55632/pwvas.v98i1.1307

Abstract

Jamie Kemman, Alani White, Holly White, Weidong Liao (Faculty Advisor), Osman Guzide (Faculty Advisor), Dept of Computer and Information Sciences, Shepherd University, Shepherdstown, WV, 25443. Safe AI Initiative: A Call for Transparency of the Impact of Generative AI and Big Data.

 

The meteoric rise in availability and adoption of generative AI tools in public and professional life has produced a dizzying array of reports on the potential benefits of AI. This has led to surging demand for funding, building, and maintaining new AI technologies, and an omnipresent push from the world’s largest tech firms to have users adopt each new development. This rise in corporate demand has been accompanied by a distinct lack of caution regarding the negative effects stemming from the unintended consequences and misaligned use of these new technologies. Our work seeks to draw attention to the full spectrum of these issues through the development of an interactive Web app, which serves as a place of education on some of the issues that stem from AI, especially from modern Generative AI. By offering an easy-to-use Web app with clear, accessible information, we hope that this site will provide users with a more informed perspective on AI, its growing role in our society, and how to use it more responsibly.

Author Biography

Weidong Liao, Shepherd University

Associate Professor of Computer and Information Sciences

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Published

2026-04-08

How to Cite

Kemman, J., White, A., White, H., Liao, W., & Guzide, O. (2026). Safe AI Initiative: A Call for Transparency of the Impact of Generative AI and Big Data. Proceedings of the West Virginia Academy of Science, 98(1). https://doi.org/10.55632/pwvas.v98i1.1307

Issue

Section

Meeting Abstracts-Poster